Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	30,912
2	Florida	28,067
3	Arizona	27,260
4	Mississippi	26,344
5	Alabama	23,802
6	Georgia	22,567
7	New York	22,354
8	South Carolina	21,860
9	New Jersey	21,565
10	Nevada	21,451
11	Texas	20,955
12	Tennessee	20,745
13	Rhode Island	20,108
14	District of Columbia	19,325
15	Arkansas	18,852
16	Massachusetts	18,283
17	Iowa	18,047
18	Illinois	17,681
19	Delaware	17,398
20	Maryland	17,395
21	California	17,114
22	Idaho	16,932
23	Nebraska	16,566
24	Utah	15,424
25	North Carolina	14,962
26	Connecticut	14,588
27	Oklahoma	13,526
28	Kansas	13,379
29	Virginia	13,312
30	Indiana	13,309
31	North Dakota	13,127
32	Wisconsin	13,010
33	South Dakota	12,914
34	Missouri	12,574
35	Minnesota	12,473
36	New Mexico	11,669
37	Michigan	10,795
38	Pennsylvania	10,486
39	Kentucky	10,308
40	Ohio	9,893
41	Washington	9,756
42	Colorado	9,672
43	Puerto Rico	9,587
44	Alaska	7,690
45	Wyoming	6,225
46	Montana	6,091
47	Oregon	5,968
48	New Hampshire	5,246
49	West Virginia	5,196
50	Hawaii	4,764
51	Maine	3,240
52	Vermont	2,509

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Mississippi	233
2	North Dakota	230
3	Alabama	209
4	South Dakota	203
5	Georgia	202
6	Puerto Rico	184
7	Tennessee	182
8	Kansas	179
9	Iowa	170
10	Hawaii	164
11	Missouri	164
12	Texas	163
13	Indiana	162
14	Nevada	162
15	Illinois	156
16	Oklahoma	149
17	Florida	148
18	North Carolina	145
19	South Carolina	140
20	Idaho	139
21	Arkansas	137
22	California	137
23	Louisiana	132
24	Minnesota	127
25	Kentucky	121
26	Virginia	108
27	Wisconsin	106
28	Alaska	102
29	Michigan	99
30	Maryland	94
31	Rhode Island	88
32	Utah	86
33	Montana	84
34	District of Columbia	80
35	Ohio	74
36	Nebraska	72
37	Arizona	68
38	Washington	65
39	Colorado	60
40	New Mexico	59
41	Delaware	58
42	Oregon	58
43	Pennsylvania	46
44	Connecticut	45
45	West Virginia	45
46	Wyoming	45
47	Massachusetts	38
48	New Jersey	33
49	New York	28
50	Maine	17
51	New Hampshire	15
52	Vermont	13

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,795
2	New York	1,670
3	Massachusetts	1,298
4	Connecticut	1,250
5	Louisiana	1,024
6	Rhode Island	977
7	District of Columbia	855
8	Mississippi	755
9	Michigan	667
10	Arizona	656
11	Illinois	641
12	Delaware	620
13	Maryland	611
14	Pennsylvania	596
15	South Carolina	487
16	Florida	484
17	Indiana	479
18	Georgia	475
19	Alabama	412
20	Texas	407
21	Nevada	389
22	New Mexico	356
23	Ohio	341
24	Colorado	334
25	Iowa	331
26	Minnesota	322
27	New Hampshire	315
28	California	310
29	Virginia	289
30	Washington	256
31	Missouri	249
32	North Carolina	244
33	Tennessee	231
34	Arkansas	230
35	Kentucky	203
36	Nebraska	198
37	Wisconsin	187
38	North Dakota	185
39	Oklahoma	184
40	South Dakota	181
41	Idaho	176
42	Kansas	148
43	Puerto Rico	122
44	Utah	121
45	Oregon	102
46	West Virginia	99
47	Maine	97
48	Vermont	92
49	Montana	85
50	Wyoming	63
51	Alaska	41
52	Hawaii	33

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Louisiana	5
2	Georgia	4
3	Arizona	3
4	Arkansas	3
5	Florida	3
6	Mississippi	3
7	South Carolina	3
8	Texas	3
9	California	2
10	Massachusetts	2
11	North Dakota	2
12	Tennessee	2
13	Alabama	1
14	Delaware	1
15	Idaho	1
16	Iowa	1
17	Kentucky	1
18	Maryland	1
19	Minnesota	1
20	Missouri	1
21	Nevada	1
22	New Mexico	1
23	North Carolina	1
24	Oklahoma	1
25	Puerto Rico	1
26	Rhode Island	1
27	Virginia	1
28	West Virginia	1
29	Alaska	0
30	Colorado	0
31	Connecticut	0
32	District of Columbia	0
33	Hawaii	0
34	Illinois	0
35	Indiana	0
36	Kansas	0
37	Maine	0
38	Michigan	0
39	Montana	0
40	Nebraska	0
41	New Hampshire	0
42	New Jersey	0
43	New York	0
44	Ohio	0
45	Oregon	0
46	Pennsylvania	0
47	South Dakota	0
48	Utah	0
49	Vermont	0
50	Washington	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	141,085	1	99
Lafayette	Florida	138,328	2	99
Lake	Tennessee	116,876	3	99
Lee	Arkansas	112,115	4	99
Lincoln	Arkansas	105,574	5	99
Davidson	Tennessee	36,485	152	95
Richland	South Carolina	24,045	410	86
Orange	California	14,582	961	69
York	South Carolina	14,307	986	68
Pierce	Washington	7,980	1760	43

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	4,612	1	99
Galax city	Virginia	4,412	2	99
Randolph	Georgia	3,983	3	99
Terrell	Georgia	3,634	4	99
McKinley	New Mexico	3,349	5	99
Richland	South Carolina	445	673	78
Davidson	Tennessee	354	848	73
Orange	California	282	1029	67
Pierce	Washington	191	1344	57
York	South Carolina	139	1586	49

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.